High-Resolution Topography (HRT) data sets are becoming increasingly available, improving our ability and opportunities to monitor geomorphic changes through multi-temporal Digital Terrain Models (DTMs). The use of repeated topographic surveys enables inferring the sediment dynamics of hazardous geomorphic processes such as floods, debris flows, and landslides, and allows us to derive important information on the risks often associated with these processes. The topographic surveying platforms, georeferencing systems, and processing tools have seen important developments in the last two decades, in particular Light Detection And Ranging (LiDAR) technology used in Airborne Laser Scanning (ALS) and Terrestrial Laser Scanning (TLS). Moreover, HRT data, produced through these techniques, changed a lot in terms of point cloud density, accuracy and precision over time. Therefore, old “legacy” data sets and recent surveys can often show comparison problems, especially when multi-temporal data are not homogeneous in terms of quality and uncertainties. In this context, data co-registration should be used to guarantee the coherence among multi-temporal surveys, minimizing, on stable areas, the distance between corresponding points acquired at different epochs. Although several studies highlight that this process is fundamental to properly compare multi-temporal DTMs, it is often not addressed in LiDAR post-processing workflows. In this paper we focus on the alignment of multi-temporal surveys in a topographically complex and rugged environment as the Moscardo debris-flow catchment (Eastern Italian Alps), testing various co-registration methods to align multi-temporal ALS point clouds (i.e. years 2003, 2009 and 2013) and the derived DTMs. In particular, we tested the pairwise registration with manual correspondences, the Iterative Closest Point (ICP) algorithm and a mathematical model that allows aligning simultaneously a generic number of point clouds, the so-called Generalized Procrustes Analysis (GPA), also in its GPA-ICP variant. Then, to correct the possible small inaccuracies generated from the gridding interpolation process, a custom-developed DTM co-registration tool (GRD-CoReg) was used to align gridded data. Both alignment phases (i.e. at point cloud and DTM level) proved to be fundamental and allowed us to obtain proper and reliable DTMs of Difference (DoDs), useful to quantify the debris mobilized and to detect the spatial and temporal patterns of catchment-scale erosion and deposition. The consistency of DoDs data was verified through the comparison between the erosion estimate of DoDs and the volumes of debris-flow events measured by the monitoring station close to the Moscardo torrent catchment outlet. The GPA-ICP algorithm followed by the GRD-CoReg tool proved to be the most effective solution for improving DoDs results with a decrease of systematic trend due to vertical and horizontal uncertainties between surveys, especially at steep slopes. The net volume difference (i.e. the sediment output from the catchment) of the 2003–2013 period changed from 3,237,896 m³ to 135,902 m³ in DoDs obtained from not co-registered and co-registered DTMs. The volume of debris flows measured at the catchment outlet during the same time interval amounts to 169,660 m³. The comparison with debris-flow volume measures at the monitoring station shows, therefore, that the DTMs obtained from the co-registration processes generate more reliable DoDs than those obtained from the raw DTMs (without the alignment).

Point cloud classification is one of the most important and time-consuming stages of airborne LiDAR (Light Detection and Ranging) data processing, playing a key role in the generation of cartographic products. This paper describes an innovative algorithm to perform LiDAR point-cloud classification, which relies on Convolutional Neural Networks (CNNs) and takes advantage of full-waveform data registered by modern laser scanners. The proposed method consists of two steps. First, a simple CNN is used to preprocess each waveform, providing a compact representation of the data. By exploiting the coordinates of the points associated with the waveforms, output vectors generated by the first CNN are then mapped into an image that is subsequently segmented by a Fully Convolutional Network (FCN): a label is assigned to each pixel and, consequently, to the point falling in the pixel. In this way, spatial positions and geometrical relationships between neighboring data are taken into account. These particular architectures allow to accurately identify even challenging classes such as power line and transmission tower.

In this paper, we present a quantitative performance investigation and repeatability assessment of a mobile robotic system for 3D mapping. With the aim of a more efficient and automatic data acquisition process with respect to well-established manual topographic operations, a 3D laser scanner coupled with an inertial measurement unit is installed on a mobile platform and used to perform a high-resolution mapping of the surrounding environment. Point clouds obtained with the use of a mobile robot are compared with those acquired with the device carried manually as well as with a terrestrial laser scanner survey that serves as a ground truth. Experimental results show that both mapping modes provide similar accuracy and repeatability, whereas the robotic system compares favorably with respect to the handheld modality in terms of noise level and point distribution. The outcomes demonstrate the feasibility of the mobile robotic platform as a promising technology for automatic and accurate 3D mapping.

Keywords: Thermal Infrared Images, 3D Building Reconstruction, Unmanned Aerial Vehicles, Photogrammetry, Computer Vision This paper addresses the problem of 3D building reconstruction from thermal infrared (TIR) images. We show that a commercial Computer Vision software can be used to automatically orient sequences of TIR images taken from an Unmanned Aerial Vehicle (UAV) and to generate 3D point clouds, without requiring any GNSS/INS data about position and attitude of the images nor camera calibration parameters. Moreover, we propose a procedure based on Iterative Closest Point (ICP) algorithm to create a model that combines high resolution and geometric accuracy of RGB images with the thermal information deriving from TIR images. The process can be carried out entirely by the aforesaid software in a simple and efficient way.

Very small glaciers (<0.5 km²) account for more than 80% of the total number of glaciers and more than 15% of the total glacier area in the European Alps. This study seeks to better understand the impact of extreme snowfall events on the resilience of very small glaciers and ice patches in the southeastern European Alps, an area with the highest mean annual precipitation in the entire Alpine chain. Mean annual precipitation here is up to 3300 mm water equivalent, and the winter snow accumulation is approximately 6.80 m at 1800 m asl averaged over the period 1979–2018. As a consequence, very small glaciers and ice/firn patches are still present in this area at rather low altitudes (1830–2340 m). We performed repeated geodetic mass balance measurements on 14 ice bodies during the period 2006–2018 and the results show an increase greater than 10% increase in ice volume over this period. This is in accordance with several extreme winter snow accumulations in the 2000s, promoting a positive mass balance in the following years. The long-term evolution of these very small glaciers and ice bodies matches well with changes in mean temperature of the ablation season linked to variability of Atlantic Multidecadal Oscillation. Nevertheless, the recent behaviour of such residual ice masses in this area where orographic precipitation represents an important component of weather amplification is somehow different to most of the Alps. We analysed synoptic meteorological conditions leading to the exceptional snowy winters in the 2000s, which appear to be related to the influence and modification of atmospheric planetary waves and Arctic Amplification, with further positive feedbacks due to change in local sea surface temperature and its interactions with low level flows and the orography. Although further summer warming is expected in the next decades, we conclude that modification of storm tracks and more frequent occurrence of extreme snowfall events during winter are crucial in ensuring the resilience of small glacial remnants in maritime alpine sectors.